An alternative form for the identification of dynamic systems with the
application of multi-objective optimization concepts, through the evolutionary
algorithm MAGO is presented. A computational tool using operational data of a
SISO system has been designed to automatically perform the construction and
selection of the best model representing it. After a data acquisition, strategies for
the system identification by parametric modelling are developed. The application
on the fitness function of appropriate criteria to choose models representing the
system is also studied. Different models (ARX, ARMAX, and OE) are built and
compared. The models obtained, by evolution, provide better fit and final
prediction error regarding that chosen by an expert. The computational effort is
low considering that the proposed method is more effective on identification of
dynamic systems. Applying this evolutionary method to more complex systems
such as MISO, MIMO, and non-linear is proposed as future work.
Keywords: computational and experimental methods, system identification,
evolutionary computation, multi-criteria decision, multi-objective optimization.